Feature-Space Generative Models for One-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2601.17905v1
• Date: Sun, 25 Jan 2026 16:45:11 GMT
• Title: Feature-Space Generative Models for One-Shot Class-Incremental Learning
• Authors: Jack Foster, Kirill Paramonov, Mete Ozay, Umberto Michieli,
• Abstract summary: Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing novel classes with limited data.<n>We propose a novel approach predicated on the hypothesis that base and novel class embeddings have structural similarity.<n>Our approach, Gen1S, consistently improves novel class recognition over the state of the art across multiple benchmarks and backbone architectures.
• Score: 33.77408261771739
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing novel classes with limited data. We focus on the challenging FSCIL setup where a model receives only a single sample (1-shot) for each novel class and no further training or model alterations are allowed after the base training phase. This makes generalization to novel classes particularly difficult. We propose a novel approach predicated on the hypothesis that base and novel class embeddings have structural similarity. We map the original embedding space into a residual space by subtracting the class prototype (i.e., the average class embedding) of input samples. Then, we leverage generative modeling with VAE or diffusion models to learn the multi-modal distribution of residuals over the base classes, and we use this as a valuable structural prior to improve recognition of novel classes. Our approach, Gen1S, consistently improves novel class recognition over the state of the art across multiple benchmarks and backbone architectures.

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